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An Explainable Student Fatigue Monitoring Module with Joint Facial Representation.

Xiaomian Li1, Jiaqin Lin2, Zhiqiang Tian3

  • 1School of Foreign Studies, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)
|April 13, 2023
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This study introduces an explainable AI model for real-time student fatigue estimation using facial analysis. The model accurately detects fatigue, improving online learning and student well-being.

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CNNMPAjoint facial representationonline fatigue detectionvideo-based online fatigue detection

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Area of Science:

  • Computer Science
  • Educational Technology
  • Cognitive Science

Background:

  • Student fatigue negatively impacts health and academic performance, necessitating effective monitoring.
  • Existing fatigue monitoring methods are often survey-based and lack continuous analysis.
  • Online education requires real-time tools to assess student engagement and well-being.

Purpose of the Study:

  • To develop and validate an explainable AI model for continuous, real-time student fatigue estimation.
  • To address the limitations of traditional, offline student fatigue monitoring techniques.
  • To enhance the quality of online higher education through proactive fatigue management.

Main Methods:

  • Proposed an explainable student fatigue estimation model integrating spatial-temporal symptom classification and data-experience joint status inference.
  • Utilized a deep convolutional neural network (CNN) for spatial-temporal feature extraction and abnormal symptom classification.
  • Employed maximum a posteriori (MAP) inference for status assessment under joint data-experience constraints.

Main Results:

  • The proposed model achieved a high accuracy rate of 94.47% on the Online Student Fatigue Monitoring (OSFM) dataset.
  • Demonstrated superior performance compared to existing methods in fatigue estimation tasks.
  • The model's explainability facilitates understanding of fatigue indicators.

Conclusions:

  • The developed model offers a significant advancement for real-time fatigue monitoring in online learning environments.
  • Findings suggest potential applications for improving both online and in-person educational strategies.
  • Continuous fatigue monitoring can mitigate adverse effects on student health and academic success.